learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Raccoon	Mar 100 Raccoon
No log	1.0	34	1.1822	0.256	0.4066	0.2145	-1.0	0.1995	0.3065	0.464	0.652	0.676	-1.0	0.5667	0.7105	0.256	0.676
No log	2.0	68	1.0477	0.2311	0.3721	0.236	-1.0	0.2353	0.2654	0.58	0.704	0.728	-1.0	0.5167	0.7947	0.2311	0.728
No log	3.0	102	0.9369	0.2892	0.505	0.2896	-1.0	0.245	0.3147	0.576	0.728	0.744	-1.0	0.5667	0.8	0.2892	0.744
No log	4.0	136	0.9132	0.2955	0.5104	0.3135	-1.0	0.4577	0.3021	0.628	0.756	0.772	-1.0	0.6167	0.8211	0.2955	0.772
No log	5.0	170	0.8618	0.2808	0.4954	0.2797	-1.0	0.29	0.3082	0.56	0.744	0.768	-1.0	0.55	0.8368	0.2808	0.768
No log	6.0	204	0.9706	0.3861	0.6914	0.4416	-1.0	0.4688	0.3999	0.592	0.728	0.744	-1.0	0.5833	0.7947	0.3861	0.744
No log	7.0	238	0.9055	0.5755	0.9064	0.6186	-1.0	0.5361	0.5908	0.684	0.74	0.756	-1.0	0.6167	0.8	0.5755	0.756
No log	8.0	272	0.9899	0.5764	0.8943	0.6448	-1.0	0.4663	0.6299	0.632	0.728	0.732	-1.0	0.4833	0.8105	0.5764	0.732
No log	9.0	306	0.8341	0.5311	0.8708	0.4887	-1.0	0.4475	0.5709	0.612	0.764	0.768	-1.0	0.6	0.8211	0.5311	0.768
No log	10.0	340	0.9214	0.5871	0.9406	0.5496	-1.0	0.4492	0.6464	0.632	0.768	0.768	-1.0	0.5667	0.8316	0.5871	0.768
No log	11.0	374	0.9724	0.6047	0.9178	0.6609	-1.0	0.4882	0.6517	0.66	0.748	0.764	-1.0	0.65	0.8	0.6047	0.764
No log	12.0	408	0.9206	0.5296	0.8574	0.6479	-1.0	0.4766	0.561	0.648	0.756	0.776	-1.0	0.65	0.8158	0.5296	0.776
No log	13.0	442	0.8629	0.615	0.9289	0.6179	-1.0	0.5005	0.6629	0.664	0.812	0.816	-1.0	0.6833	0.8579	0.615	0.816
No log	14.0	476	0.9147	0.6044	0.9447	0.6536	-1.0	0.5368	0.6338	0.66	0.752	0.776	-1.0	0.65	0.8158	0.6044	0.776
0.7228	15.0	510	0.8777	0.6027	0.9514	0.5969	-1.0	0.485	0.6659	0.636	0.78	0.788	-1.0	0.6167	0.8421	0.6027	0.788
0.7228	16.0	544	0.7731	0.6327	0.9617	0.6351	-1.0	0.5116	0.6957	0.668	0.792	0.812	-1.0	0.6667	0.8579	0.6327	0.812
0.7228	17.0	578	0.8076	0.6626	0.9634	0.692	-1.0	0.5305	0.7215	0.676	0.792	0.836	-1.0	0.7167	0.8737	0.6626	0.836
0.7228	18.0	612	0.8748	0.6455	0.9676	0.7353	-1.0	0.4687	0.7132	0.66	0.784	0.804	-1.0	0.6333	0.8579	0.6455	0.804
0.7228	19.0	646	0.8480	0.6187	0.952	0.6912	-1.0	0.5026	0.6682	0.68	0.768	0.808	-1.0	0.6833	0.8474	0.6187	0.808
0.7228	20.0	680	0.8820	0.6162	0.9433	0.6875	-1.0	0.5024	0.659	0.676	0.768	0.808	-1.0	0.65	0.8579	0.6162	0.808
0.7228	21.0	714	0.8377	0.66	0.9665	0.7059	-1.0	0.5209	0.7157	0.692	0.776	0.828	-1.0	0.6833	0.8737	0.66	0.828
0.7228	22.0	748	0.8533	0.6266	0.9726	0.6768	-1.0	0.5198	0.6798	0.672	0.768	0.828	-1.0	0.6833	0.8737	0.6266	0.828
0.7228	23.0	782	0.8081	0.6583	0.9696	0.7272	-1.0	0.5421	0.7118	0.692	0.776	0.832	-1.0	0.7	0.8737	0.6583	0.832
0.7228	24.0	816	0.8054	0.66	0.9673	0.7331	-1.0	0.5491	0.7085	0.704	0.776	0.828	-1.0	0.7	0.8684	0.66	0.828
0.7228	25.0	850	0.8162	0.6515	0.9692	0.6885	-1.0	0.5157	0.7075	0.688	0.772	0.832	-1.0	0.7	0.8737	0.6515	0.832
0.7228	26.0	884	0.7957	0.6344	0.9596	0.6593	-1.0	0.5253	0.6874	0.688	0.784	0.84	-1.0	0.6833	0.8895	0.6344	0.84
0.7228	27.0	918	0.8075	0.6385	0.9556	0.6865	-1.0	0.523	0.6909	0.692	0.78	0.836	-1.0	0.7	0.8789	0.6385	0.836
0.7228	28.0	952	0.8124	0.6376	0.9576	0.6839	-1.0	0.5231	0.6871	0.692	0.784	0.836	-1.0	0.7	0.8789	0.6376	0.836
0.7228	29.0	986	0.8056	0.6463	0.9616	0.6976	-1.0	0.5236	0.6979	0.692	0.78	0.836	-1.0	0.7	0.8789	0.6463	0.836
0.4249	30.0	1020	0.8052	0.6463	0.9616	0.6976	-1.0	0.5236	0.6979	0.692	0.78	0.836	-1.0	0.7	0.8789	0.6463	0.836

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

mipedro1/yolo_raccoon_detector

yolo_raccoon_detector

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mipedro1/
yolo_raccoon_detector